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* unify pipeline signature with existing example * iwyu * move stuff around in load-tile-transpose * cleanups in batched transpose pipeline * comments * use same inputs size * cleaner printf * print host args * use 64 block sides in the 37_transpose example * roll back grid dimension size adjustment for 37_transpose example * transpose grid for 37_transpose to unify with 35_batched_transpose * unify grid computation logic * make policy methods device only (since they are used only on device from the pipeline) * more host/device attribute cleanups * copy over problem * move over pipeline and policy * add switch to batched transpose api * make the lds problem more similar to original problem * factor out logic into traits * factor out conditional compilation into trait parameter * propagate pipeline to args * unhardcode pipeline dispatch parameter * refactor vector size * put warp tile out of dispatch * rename template parameter for trait * rewrite vector size in terms of problem * mark policy-internal struct variable as device * factor out input distribution and thread access pattern from policies * reword vector size * use datatype across batched transpose pipelines, problems and kernel * remove transpose traits from lds pipeline * add padding to the lds pipeline *interface* * add comment * remove ck_tile example #37 * update cmakelists * add test for new pipeline * update batched transpose test * roll back load_tile_transpose changes * remove comments * pack dispatch parameters into a config * padM can be enabled * adjust lds vector size to enable padding along N * update test * clean up logic * swap m/n input vector size * adjust perf test script * sweep over C/W in perf test * count both read and written bytes into bandwidth (x2 the number) * clang-format * widen size range for perf test * remove 64k x 64k case; it's too large for index * remove thread tile from dispatch * Solve merge conflict * fix compile * modify the transpose * solve the test error and clang format * Add v3 support for Groupd fwd conv+bias+clamp & ckProfiler (#2463) * Add logging to IsSupported. * Less casting in AddClamp * Conv+bias+clamp instances & profiler BF16 * Fix 3D instances & run just 1x for verification. * :Run just once for verification conv fwd. * ckProfiler conv fwd clampwq * Remove exec bit & formatting * Add support for MultiD for grouped conv fwd v3. * Enable 2Lds. * clean * align instances * align instances * profiler fixes * Fixes * fix * fix --------- Co-authored-by: Adam Osewski <root@quanta-ccs-aus-f01-19.cs-aus.dcgpu> Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Fixing 0ms and inf GB/s issue in img2col (#2565) issue : ==== ``` sh $ bin/tile_example_img2col Perf: 0 ms, inf GB/s ``` solution : ====== Problem occured because config.time_kernel is false by default. if false, then no need to calculate perf, just print proper message `image_to_coloumn: pass, No Perf generated due to config.time_kernel=0` * merge with develop * solve clang format --------- Co-authored-by: ThomasNing <thomas.ning@amd.com> Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com> Co-authored-by: Adam Osewski <root@quanta-ccs-aus-f01-19.cs-aus.dcgpu> Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> Co-authored-by: rahjain-amd <Rahul.Jain@amd.com>
Batched Transpose
This folder contains example for batched Transpose using ck_tile tile-programming implementation. Currently, it supports the batched transpose with NCHW to NHWC or NHWC to NCHW. So in this way from NCHW you could transpose to either NHWC or NWCH(two transposes). Now the transpose read with single data point. We would soon put it in vectorized transpose.
build
# in the root of ck_tile
mkdir build && cd build
# you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank
sh ../script/cmake-ck-dev.sh ../ <arch>
# Make the transpose executable
make tile_example_batched_transpose -j
This will result in an executable build/bin/tile_example_batched_transpose
example
args:
-N input batch size (default:2)
-C input channel size. (default:16)
-H input height size. (default:1)
-W input width size. (default:16)
-v whether do CPU validation or not (default: 1)
-layout_in input tensor data layout - NCHW by default
-layout_out output tensor data layout - NHWC by default
-seed seed to be used, -1 means random every time (default:-1)
-k_name t to 1 will print kernel name (default:0)
-warmup warmup iterations to run this kernel (default:50)
-repeat number of iterations to run this kernel (default:100)